In this report, we describe the Unified Cyber Security ontology (UCO) to support situational awareness in cyber security systems. The ontology is an effort to incorporate and integrate heterogeneous information available from different cyber security systems and most commonly used cyber security standards for information sharing and exchange. The ontology has also been mapped to a number of existing cyber security ontologies as well as concepts in the Linked Open Data cloud. Similar to DBpedia which serves as the core for Linked Open Data cloud, we envision UCO to serve as the core for the specialized cyber security Linked Open Data cloud which would evolve and grow with the passage of time with additional cybersecurity data sets as they become available. We also present a prototype system and concrete use-cases supported by the UCO ontology. To the best of our knowledge, this is the first cyber security ontology that has been mapped to general world ontologies to support broader and diverse security use-cases. We compare the resulting ontology with previous efforts, discuss its strengths and limitations, and describe potential future work directions.

Extracting Structured Summaries
from Text Documents

Dr. Zareen Syed
Research Assistant Professor, UMBC

10:30am, Monday, 9 November 2015, ITE 346, UMBC

In this talk, Dr. Syed will present unsupervised approaches for automatically extracting structured summaries composed of slots and fillers (attributes and values) and important facts from articles, thus effectively reducing the amount of time and effort spent on gathering intelligence by humans using traditional keyword based search approaches. The approach first extracts important concepts from text documents and links them to unique concepts in Wikitology knowledge base. It then exploits the types associated with the linked concepts to discover candidate slots and fillers. Finally it applies specialized approaches for ranking and filtering slots to select the most relevant slots to include in the structured summary.

Compared with the state of the art, Dr. Syed’s approach is unrestricted, i.e., it does not require manually crafted catalogue of slots or relations of interest that may vary over different domains. Unlike Natural Language Processing (NLP) based approaches that require well-formed sentences, the approach can be applied on semi-structured text. Furthermore, NLP based approaches for fact extraction extract lexical facts and sentences that require further processing for disambiguating and linking to unique entities and concepts in a knowledge base, whereas, in Dr. Syed’s approach, concept linking is done as a first step in the discovery process. Linking concepts to a knowledge base provides the additional advantage that the terms can be explicitly linked or mapped to semantic concepts in other ontologies and are thus available for reasoning in more sophisticated language understanding systems.

While clinical text NLP systems have become very effective in recognizing named entities in clinical text and mapping them to standardized terminologies in the normalization process, there remains a gap in the ability of extractors to combine entities together into a complete semantic representation of medical concepts that contain multiple attributes each of which has its own set of allowed named entities or values. Furthermore, additional domain knowledge may be required to determine the semantics of particular tokens in the text that take on special meanings in relation to this concept. This research proposes an approach that provides ontological mappings of the surface forms of medical concepts that are of the UMLS semantic class signs/symptoms. The mappings are used to extract and encode the constituent set of named entities into interoperable semantic structures that can be linked to other structured and unstructured data for reuse in research and analysis.

Coldstart is a task in the NIST Text Analysis Conference’s Knowledge Base Population suite that combines entity linking and slot filling to populate an empty knowledge base using a predefined ontology for the facts and relations. This paper describes a system developed by the Human Language Technology Center of Excellence at Johns Hopkins University for the 2014 Coldstart task.

The JHU HLTCOE participated in the Cold Start task in this year’s Text Analysis Conference Knowledge Base Population evaluation. This is our third year of participation in the task, and we continued our research with the KELVIN system. We submitted experimental variants that explore use of forward-chaining inference, slightly more aggressive entity clustering, refined multiple within-document conference, and prioritization of relations extracted from news sources.

The NSF-sponsored Platys project explored the idea that places are more than just GPS coordinates. They are concepts rich with semantic information, including people, activities, roles, functions, time and purpose. Our mobile phones can learn to recognize the places we are in and use information about them to provide better services.

The Platys project focuses on developing a high-level, semantic notion of location called place. A place, unlike a geospatial position, derives its meaning from a user’s actions and interactions in addition to the physical location where it occurs. Our aim is to enable the construction of a large variety of applications that take advantage of place to render relevant content and functionality and, thus, improve user experience. We consider elements of context that are particularly related to mobile computing. The main problems we have addressed to realize our place-oriented mobile computing vision are representing places, recognizing places, and engineering place-aware applications. We describe the approaches we have developed for addressing these problems and related subproblems. A key element of our work is the use of collaborative information sharing where users’ devices share and integrate knowledge about places. Our place ontology facilitates such collaboration. Declarative privacy policies allow users to specify contextual features under which they prefer to share or not share their information.

Information Extraction from Dirty Notesfor Clinical Decision Support

Clare Grasso

10:00am Tuesday, 12 May 2015, ITE346

The term clinical decision support refers broadly to providing clinicians or patients with computer-generated clinical knowledge and patient-related information, intelligently filtered or presented at appropriate times, to enhance patient care. It is estimated that at least 50% of the clinical information describing a patient’s current condition and stage of therapy resides in the free-form text portions of the Electronic Health Record (EHR). Both linguistic and statistical natural language processing (NLP) models assume the presence of a formal underlying grammar in the text. Yet, clinical notes are often times filled with overloaded and nonstandard abbreviations, sentence fragments, and creative punctuation that make it difficult for grammar-based NLP systems to work effectively. This research focuses on investigating scalable machine learning and semantic techniques that do not rely on an underlying grammar to extract medical concepts in the text in order to apply them in CDS on commodity hardware and software systems. Additionally, by packaging the extracted data within a semantic knowledge representation, the facts can be combined with other semantically encoded facts and reasoned over to help to inform clinicians in their decision making.

In this weeks ebiquity lab meeting, Ankur Padia will talk about ontology learning and the work he did for his MS thesis at 10:00am in ITE 346 at UMBC.

Ontology Learning

Ankur Padia

10:00am Tuesday, Apr. 28, 2015, ITE 346

Ontology Learning has been the subject of intensive study for the past decade. Researchers in this field have been motivated by the possibility of automatically building a knowledge base on top of text documents so as to support reasoning based knowledge extraction. While most works in this field have been primarily statistical (known as light-weight Ontology Learning) not much attempt has been made in axiomatic Ontology Learning (called Formal Ontology Learning) from Natural Language text documents. Presentation will focus on the relationship between Description Logic and Natural Language (limited to IS-A) for Formal Ontology Learning.

Ph.D. Dissertation Defense

A Semantic Resolution Framework for Integrating
Manufacturing Service Capability Data

10:00am Monday 27 April 2015, ITE 217b

Building flexible manufacturing supply chains requires availability of interoperable and accurate manufacturing service capability (MSC) information of all supply chain participants. Today, MSC information, which is typically published either on the supplier’s web site or registered at an e-marketplace portal, has been shown to fall short of interoperability and accuracy requirements. The issue of interoperability can be addressed by annotating the MSC information using shared ontologies. However, this ontology-based approach faces three main challenges: (1) lack of an effective way to automatically extract a large volume of MSC instance data hidden in the web sites of manufacturers that need to be annotated; (2) difficulties in accurately identifying semantics of these extracted data and resolving semantic heterogeneities among individual sources of these data while integrating them under shared formal ontologies; (3) difficulties in the adoption of ontology-based approaches by the supply chain managers and users because of their unfamiliarity with the syntax and semantics of formal ontology languages such as the web ontology language (OWL).

The objective of our research is to address the main challenges of ontology-based approaches by developing an innovative approach that is able to extract MSC instances from a broad range of manufacturing web sites that may present MSC instances in various ways, accurately annotate MSC instances with formal defined semantics on a large scale, and integrate these annotated MSC instances into formal manufacturing domain ontologies to facilitate the formation of supply chains of manufacturers. To achieve this objective, we propose a semantic resolution framework (SRF) that consists of three main components: a MSC instance extractor, a MSC Instance annotator and a semantic resolution knowledge base. The instance extractor builds a local semantic model that we call instance description model (IDM) for each target manufacturer web site. The innovative aspect of the IDM is that it captures the intended structure of the target web site and associates each extracted MSC instance with a context that describes possible semantics of that instance. The instance annotator starts the semantic resolution by identifying the most appropriate class from a (or a set of) manufacturing domain ontology (or ontologies) (MDO) to annotate each instance based on the mappings established between the context of that instance and the vocabularies (i.e., classes and properties) defined in the MDO. The primary goal of the semantic resolution knowledge base (SR-KB) is to resolve semantic heterogeneity that may occur in the instance annotation process and thus improve the accuracy of the annotated MSC instances. The experimental results demonstrate that the instance extractor and the instance annotator can effectively discover and annotate MSC instances while the SR-KB is able to improve both precision and recall of annotated instances and reducing human involvement along with the evolution of the knowledge base.

The theme of the 2015 Ontology Summit is Internet of Things: Toward Smart Networked Systems and Societies. The Ontology Summit is an annual series of events (first started by Ontolog and NIST in 2006) that involve the ontology community and communities related to each year’s theme.

The 2015 Summit will hold a virtual discourse over the next three months via mailing lists and online panel sessions augmented conference calls. The Summit will culminate in a two-day face-to-face workshop on 13-14 April 2015 in Arlington, VA. The Summit’s goal is to explore how ontologies can play a significant role in the realization of smart networked systems and societies in the Internet of Things.

The Summit’s initial launch session will take place from 12:30pm to 2:00pm EDT on Thursday, January 15th and will include overview presentations from each of the four technical tracks. See the 2015 Ontology Summit for more information, the schedule and details on how to participate in these free an open events.

Dissertation Defense

TABEL — A Domain Independent and Extensible Frameworkfor Inferring the Semantics of Tables

8:00am Thursday, 8 January 2015, ITE325b

Tables are an integral part of documents, reports and Web pages in many scientific and technical domains, compactly encoding important information that can be difficult to express in text. Table-like structures outside documents, such as spreadsheets, CSV files, log files and databases, are widely used to represent and share information. However, tables remain beyond the scope of regular text processing systems which often treat them like free text.

This dissertation presents TABEL — a domain independent and extensible framework to infer the semantics of tables and represent them as RDF Linked Data. TABEL captures the intended meaning of a table by mapping header cells to classes, data cell values to existing entities and pair of columns to relations from an given ontology and knowledge base. The core of the framework consists of a module that represents a table as a graphical model to jointly infer the semantics of headers, data cells and relation between headers. We also introduce a novel Semantic Message Passing scheme, which incorporates semantics into message passing, to perform joint inference over the probabilistic graphical model. We also develop and explore a “human-in-the-loop” paradigm, presenting plausible models of user interaction with our framework and its impact on the quality of inferred semantics.

We present techniques that are both extensible and domain agnostic. Our framework supports the addition of preprocessing modules without affecting existing ones, making TABEL extensible. It also allows background knowledge bases to be adapted and changed based on the domains of the tables, thus making it domain independent. We demonstrate the extensibility and domain independence of our techniques by developing an application of TABEL in the healthcare domain. We develop a proof of concept for an application to generate meta-analysis reports automatically, which is built on top of the semantics inferred from tables found in medical literature.

A thorough evaluation with experiments over dataset of tables from the Web and medical research reports presents promising results.

Evidence-based medicine is the application of current medical evidence to patient care and typically uses quantitative data from research studies. It is increasingly driven by data on the efficacy of drug dosages and the correlations between various medical factors that are assembled and integrated through meta–analyses (i.e., systematic reviews) of data in tables from publications and clinical trial studies. We describe a important component of a system to automatically produce evidence reports that performs two key functions: (i) understanding the meaning of data in medical tables and (ii) identifying and retrieving relevant tables given a input query. We present modifications to our existing framework for inferring the semantics of tables and an ontology developed to model and represent medical tables in RDF. Representing medical tables as RDF makes it easier for the automatic extraction, integration and reuse of data from multiple studies, which is essential for generating meta–analyses reports. We show how relevant tables can be identified by querying over their RDF representations and describe two evaluation experiments: one on mapping medical tables to linked data and another on identifying tables relevant to a retrieval query.

In The trouble with DBpedia, Paul Houle talks about the problems he sees in DBpedia, Freebase and Wikidata and offers up :BaseKB as a better “generic database” that models concepts that are in people’s shared consciousness.

:BaseKB is a purified version of Freebase which is compatible with industry-standard RDF tools. By removing hundreds of millions of duplicate, invalid, or unnecessary facts, :BaseKB users speed up their development cycles dramatically when compared to the source Freebase dumps.

:BaseKB is available for commercial and academic use under a CC-BY license. Weekly versions (:BaseKB Now) can be downloaded from Amazon S3 on a “requester-paid basis”, estimated at $3.00US per download. There are also BaseKB Gold releases which are periodic :BaseKB Now snapshots. These can be downloaded free via Bittorrent or purchased as a Blu Ray disc.